Capacity Evaluation of Diagnostic Tests For COVID-19 Using Multicriteria Decision-Making Techniques.


Journal

Computational and mathematical methods in medicine
ISSN: 1748-6718
Titre abrégé: Comput Math Methods Med
Pays: United States
ID NLM: 101277751

Informations de publication

Date de publication:
2020
Historique:
received: 11 05 2020
revised: 22 06 2020
accepted: 07 07 2020
entrez: 18 8 2020
pubmed: 18 8 2020
medline: 1 9 2020
Statut: epublish

Résumé

In December 2019, cases of pneumonia were detected in Wuhan, China, which were caused by the highly contagious coronavirus. This study is aimed at comparing the confusion regarding the selection of effective diagnostic methods to make a mutual comparison among existing SARS-CoV-2 diagnostic tests and at determining the most effective one. Based on available published evidence and clinical practice, diagnostic tests of coronavirus disease (COVID-19) were evaluated by multi-criteria decision-making (MCDM) methods, namely, fuzzy preference ranking organization method for enrichment evaluation (fuzzy PROMETHEE) and fuzzy technique for order of preference by similarity to ideal solution (fuzzy TOPSIS). Computerized tomography of chest (chest CT), the detection of viral nucleic acid by polymerase chain reaction, cell culture, CoV-19 antigen detection, CoV-19 antibody IgM, CoV-19 antibody IgG, and chest X-ray were evaluated by linguistic fuzzy scale to compare among the diagnostic tests. This scale consists of selected parameters that possessed different weights which were determined by the experts' opinions of the field. The results of our study with both proposed MCDM methods indicated that the most effective diagnosis method of COVID-19 was chest CT. It is interesting to note that the methods that are consistently used in the diagnosis of viral diseases were ranked in second place for the diagnosis of COVID-19. However, each country should use appropriate diagnostic solutions according to its own resources. Our findings also show which diagnostic systems can be used in combination.

Identifiants

pubmed: 32802146
doi: 10.1155/2020/1560250
pmc: PMC7411452
doi:

Substances chimiques

Immunoglobulin G 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

1560250

Informations de copyright

Copyright © 2020 Murat Sayan et al.

Déclaration de conflit d'intérêts

The authors declare that there is no conflict of interest regarding the publication of this paper.

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Auteurs

Murat Sayan (M)

Faculty of Medicine, Clinical Laboratory, PCR Unit, Kocaeli University, Kocaeli, Turkey.
DESAM Institute, Near East University, Nicosia/TRNC, Mersin-10, 99138, Turkey.

Figen Sarigul Yildirim (F)

Health Science University, Antalya Education and Research Hospital, Department of Infectious Diseases and Clinical Microbiology, Antalya 07050, Turkey.

Tamer Sanlidag (T)

DESAM Institute, Near East University, Nicosia/TRNC, Mersin-10, 99138, Turkey.
Department of Medical Microbiology, Manisa Celal Bayar University, Manisa, Turkey.

Berna Uzun (B)

DESAM Institute, Near East University, Nicosia/TRNC, Mersin-10, 99138, Turkey.
Department of Mathematics, Near East University, Nicosia/TRNC, Mersin-10, 99138, Turkey.

Dilber Uzun Ozsahin (D)

DESAM Institute, Near East University, Nicosia/TRNC, Mersin-10, 99138, Turkey.
Department of Biomedical Engineering, Faculty of Engineering, Near East University, Nicosia/TRNC, Mersin-10, 99138, Turkey.

Ilker Ozsahin (I)

DESAM Institute, Near East University, Nicosia/TRNC, Mersin-10, 99138, Turkey.
Department of Biomedical Engineering, Faculty of Engineering, Near East University, Nicosia/TRNC, Mersin-10, 99138, Turkey.

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